MHINDR -- a DSM5 based mental health diagnosis and recommendation framework using LLM
This addresses mental health diagnosis and recommendation for practitioners and patients using online forums, but appears incremental as it combines existing LLM and DSM-5 approaches.
The authors tackled mental health diagnosis from user-generated text by proposing MHINDR, an LLM-based framework integrated with DSM-5 criteria, which analyzes text to diagnose conditions and generate personalized interventions for practitioners, emphasizing temporal information extraction and psychological features for symptom tracking and summaries.
Mental health forums offer valuable insights into psychological issues, stressors, and potential solutions. We propose MHINDR, a large language model (LLM) based framework integrated with DSM-5 criteria to analyze user-generated text, dignose mental health conditions, and generate personalized interventions and insights for mental health practitioners. Our approach emphasizes on the extraction of temporal information for accurate diagnosis and symptom progression tracking, together with psychological features to create comprehensive mental health summaries of users. The framework delivers scalable, customizable, and data-driven therapeutic recommendations, adaptable to diverse clinical contexts, patient needs, and workplace well-being programs.